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High-performance simplification of triangular surfaces using a GPU.

Mohamed H Mousa1,2, Mohamed K Hussein1

  • 1Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.

Plos One
|August 5, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a GPU-accelerated mesh simplification algorithm. It enhances speed and interactivity for geometric modeling and visualization by leveraging parallel computing, preserving mesh topology.

Area of Science:

  • Computer Graphics
  • High-Performance Computing
  • Geometric Modeling

Background:

  • Mesh simplification is crucial for geometric modeling and visualization, with decades of research.
  • Recent GPU advancements offer significant speed and interactivity improvements.
  • Existing techniques face challenges with dynamic memory and parallelization.

Purpose of the Study:

  • To develop a novel mesh simplification algorithm utilizing the parallel processing capabilities of modern GPUs.
  • To adapt the halfedge data structure for CUDA's dynamic memory constraints.
  • To accelerate mesh simplification while maintaining topological integrity.

Main Methods:

  • Customization of the halfedge data structure for CUDA memory limitations.
  • Full parallelization using a lock-free skip priority queue.

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  • Implementation utilizing disjoint mesh regions for concurrent processing.

    • Main Results:

    • The proposed algorithm demonstrates accelerated mesh simplification.
    • Preservation of the mesh's topological properties is achieved.
    • Experimental results and comparisons validate the algorithm's efficiency.

    Conclusions:

    • The GPU-parallelized mesh simplification technique offers significant performance gains.
    • The method effectively balances speed with the preservation of mesh topology.
    • This approach advances geometric modeling and visualization applications through efficient parallel processing.